Abstract:
In recent years, technological advances and the latest trends in communication and
control systems engineering have driven the revolution for multi-agent systems with
distributed control through consensus algorithms for various applications. Numerous
real-time problems and applications are associated with the complex interconnection
control of the distributed multi-agent systems. As the scalability of the networks is
expanding dramatically so it is important to understand the dynamic behavior of networked systems for continuous availability. These systems require subsystems at the
agent level to make autonomous decisions by exchanging information with neighboring agents to achieve their local and global convergence objectives. Simultaneously,
this leads to practical and theoretical challenges in multi-agents due to their wireless
nature, limited bandwidth, communication delays, random topologies, and limited
resources.
One of the objectives of this Ph.D. study was to develop an efficient consensus algorithm for distributed cooperative control of the multi-agent systems. Also, the
author focus was to find the agents in the network by effective estimation. A fully
distributed autonomous framework is considered where agents are restrained with
limited communication resources. Distributed consensus algorithms are addressed
for fixed and dynamic network topologies that quickly generate arbitrarily accurate
estimates. Reliable and unreliable communication was also taken into account in the
asynchronous time update in the communication network. Throughout this thesis,
matrix and graphic theories are used as prerequisites for the design and analysis of
distributed algorithms. Necessary robust convergence conditions are considered to
ensure the multi-agent-system dynamics to seek the convergence.
Finally, the proposed algorithm is utilized for spectrum sensing in a cognitive radio
network. For a particular goal, this research present an idea for energy and network
model. The proposed energy model is responsible for spectrum sensing of a cognitive
radio network to determine the presence of primary users. As the spectrum is detected
free or occupied, secondary users of the network build efficient topologies that reduce
both network latency and interruptions for appropriate measurements in reaching
consensus.
In the end, sufficient conditions for the convergence along with the best possible estimates are computed for the proposed and the existing systems. Also, Improvements
and validation of the proposed algorithms in multiple network scenarios are tabulated
at the end of each chapter. Furthermore, numerical gain analysis and the simulation
results reflect the effectiveness and efficiency of the proposed method based on the
key parameter indicators, which includes the total number of iterations, processing
time, asymptotic convergence factor, and asymptotic convergence time. It is worth
mentioning here that maximum 45% performance gain is achieved by the proposed
method while comparing with metropolis method and similarly, maximum 37% performance gain is achieved when comparing with the local degree weights.